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Module

CEG1713 : Data Science 1

  • Offered for Year: 2020/21
  • Module Leader(s): Professor Philip James
  • Owning School: Engineering
  • Teaching Location: Newcastle City Campus
Semesters
Semester 1 Credit Value: 10
ECTS Credits: 5.0

Aims

This module will provide students with the foundations to manipulate digital data and carry out computations using programs and scripts. It provides an introduction to fundamental programming principles including data processing and input and output. It utilises a number of current scripting tools, languages and packages that are essential to the spatial scientist including Python.

Outline Of Syllabus

his module covers:
•       Python syntax and tools
•       Data types and calculations
•       Conditional statements and branching
•       Loops and repetition
•       Data in lists
•       File input and output
•       Libraries and scripts

Teaching Methods

Please note that module leaders are reviewing the module teaching and assessment methods for Semester 2 modules, in light of the Covid-19 restrictions. There may also be a few further changes to Semester 1 modules. Final information will be available by the end of August 2020 in for Semester 1 modules and the end of October 2020 for Semester 2 modules.

Teaching Activities
Category Activity Number Length Student Hours Comment
Scheduled Learning And Teaching ActivitiesLecture11:001:00N/A
Guided Independent StudyAssessment preparation and completion210:0020:002 reports and associated scripts.
Structured Guided LearningLecture materials180:206:00Directed reading and research
Scheduled Learning And Teaching ActivitiesPractical31:003:00PC practical / support session
Guided Independent StudyDirected research and reading91:009:00N/A
Guided Independent StudySkills practice32:006:00Self-supported practicals with worked examples
Scheduled Learning And Teaching ActivitiesDrop-in/surgery91:009:00Weekly Q & A
Guided Independent StudyIndependent study180:206:00Practice of material delivered through lectures.
Guided Independent StudyIndependent study140:0040:00Background reading and reading of lecture notes for a full understanding of material
Total100:00
Teaching Rationale And Relationship

•       Students will be presented with new information and concepts through lectures. Using interactive tools the students will be able to practice key concepts during the lectures
•       Practicals will directly address key programming constructs and problems and provide practice in data handling and analysis
•       Lectures and practicals will be used to demonstrate the Python scripting language and the Jupyter notebook environment for data science
•       Practicals will demonstrate the use of external libraries.

Alternatives will be offered to students unable to be present-in-person due to the prevailing C-19 circumstances.
Student’s should consult their individual timetable for up-to-date delivery information.

Assessment Methods

Please note that module leaders are reviewing the module teaching and assessment methods for Semester 2 modules, in light of the Covid-19 restrictions. There may also be a few further changes to Semester 1 modules. Final information will be available by the end of August 2020 in for Semester 1 modules and the end of October 2020 for Semester 2 modules.

The format of resits will be determined by the Board of Examiners

Other Assessment
Description Semester When Set Percentage Comment
Report1M100Script output, testing and reflective analysis. Approx 2 x 10 hours work (50% each)
Assessment Rationale And Relationship

•       The coursework elements will assess the students’ ability to solve problems through the creation of scripts utilising data processing.
•       The coursework elements will require the employ of external libraries to solve some of the problems set

Reading Lists

Timetable